Abstract
Background: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings: In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning.
Original language | English |
---|---|
Pages (from-to) | 1923-1994 |
Number of pages | 72 |
Journal | The Lancet |
Volume | 392 |
Issue number | 10159 |
DOIs | |
Publication status | Published - 10 Nov 2018 |
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In: The Lancet, Vol. 392, No. 10159, 10.11.2018, p. 1923-1994.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017
T2 - a systematic analysis for the Global Burden of Disease Study 2017
AU - GBD 2017 Risk Factor Collaborators
AU - Stanaway, Jeffrey D.
AU - Afshin, Ashkan
AU - Gakidou, Emmanuela
AU - Lim, Stephen S.
AU - Abate, Degu
AU - Abate, Kalkidan Hassen
AU - Abbafati, Cristiana
AU - Abbasi, Nooshin
AU - Abbastabar, Hedayat
AU - Abd-Allah, Foad
AU - Abdela, Jemal
AU - Abdelalim, Ahmed
AU - Abdollahpour, Ibrahim
AU - Abdulkader, Rizwan Suliankatchi
AU - Abebe, Molla
AU - Abebe, Zegeye
AU - Abera, Semaw F.
AU - Abil, Olifan Zewdie
AU - Abraha, Haftom Niguse
AU - Abrham, Aklilu Roba
AU - Abu-Raddad, Laith Jamal
AU - Abu-Rmeileh, Niveen ME
AU - Accrombessi, Manfred Mario Kokou
AU - Acharya, Dilaram
AU - Acharya, Pawan
AU - Adamu, Abdu A.
AU - Adane, Akilew Awoke
AU - Adebayo, Oladimeji M.
AU - Adedoyin, Rufus Adesoji
AU - Adekanmbi, Victor
AU - Ademi, Zanfina
AU - Adetokunboh, Olatunji O.
AU - Adib, Mina G.
AU - Admasie, Amha
AU - Adsuar, Jose C.
AU - Afanvi, Kossivi Agbelenko
AU - Afarideh, Mohsen
AU - Agarwal, Gina
AU - Aggarwal, Anju
AU - Aghayan, Sargis Aghasi
AU - Agrawal, Anurag
AU - Agrawal, Sutapa
AU - Ahmadi, Alireza
AU - Ahmadi, Mehdi
AU - Ahmadieh, Hamid
AU - Ahmed, Muktar Beshir
AU - Aichour, Amani Nidhal
AU - Aichour, Ibtihel
AU - Aichour, Miloud Taki Eddine
AU - Akbari, Mohammad Esmaeil
AU - Akinyemiju, Tomi
AU - Akseer, Nadia
AU - Al-Aly, Ziyad
AU - Al-Eyadhy, Ayman
AU - Al-Mekhlafi, Hesham M.
AU - Alahdab, Fares
AU - Alam, Khurshid
AU - Alam, Samiah
AU - Alam, Tahiya
AU - Alashi, Alaa
AU - Alavian, Seyed Moayed
AU - Alene, Kefyalew Addis
AU - Ali, Komal
AU - Ali, Syed Mustafa
AU - Alijanzadeh, Mehran
AU - Alizadeh-Navaei, Reza
AU - Aljunid, Syed Mohamed
AU - Alkerwi, Ala'a
AU - Alla, François
AU - Alsharif, Ubai
AU - Altirkawi, Khalid
AU - Alvis-Guzman, Nelson
AU - Amare, Azmeraw T.
AU - Ammar, Walid
AU - Anber, Nahla Hamed
AU - Anderson, Jason A.
AU - Andrei, Catalina Liliana
AU - Androudi, Sofia
AU - Animut, Megbaru Debalkie
AU - Anjomshoa, Mina
AU - Ansha, Mustafa Geleto
AU - Antó, Josep M.
AU - Antonio, Carl Abelardo T.
AU - Anwari, Palwasha
AU - Appiah, Lambert Tetteh
AU - Appiah, Seth Christopher Yaw
AU - Arabloo, Jalal
AU - Aremu, Olatunde
AU - Ärnlöv, Johan
AU - Artaman, Al
AU - Aryal, Krishna K.
AU - Asayesh, Hamid
AU - Ataro, Zerihun
AU - Ausloos, Marcel
AU - Avokpaho, Euripide F.G.A.
AU - Awasthi, Ashish
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AU - Ayer, Rakesh
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AU - Babazadeh, Arefeh
AU - Badali, Hamid
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AU - Basu, Sanjay
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AU - Bazargan-Hejazi, Shahrzad
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AU - Belay, Ezra
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AU - Beuran, Mircea
AU - Beyranvand, Tina
AU - Bhala, Neeraj
AU - Bhalla, Ashish
AU - Bhattarai, Suraj
AU - Bhutta, Zulfiqar A.
AU - Biadgo, Belete
AU - Bijani, Ali
AU - Bikbov, Boris
AU - Bilano, Ver
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AU - Campos-Nonato, Ismael R.
AU - Cárdenas, Rosario
AU - Carreras, Giulia
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AU - Carvalho, Félix
AU - Castañeda-Orjuela, Carlos A.
AU - Castillo Rivas, Jacqueline
AU - Castro, Franz
AU - Catalá-López, Ferrán
AU - Causey, Kate
AU - Cercy, Kelly M.
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AU - Chaiah, Yazan
AU - Chang, Hsing Yi
AU - Chang, Jung Chen
AU - Chang, Kai Lan
AU - Charlson, Fiona J.
AU - Chattopadhyay, Aparajita
AU - Chattu, Vijay Kumar
AU - Chee, Miao Li
AU - Cheng, Ching Yu
AU - Chew, Adrienne
AU - Chiang, Peggy Pei Chia
AU - Chimed-Ochir, Odgerel
AU - Chin, Ken Lee
AU - Chitheer, Abdulaal
AU - Choi, Jee Young J.
AU - Chowdhury, Rajiv
AU - Christensen, Hanne
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AU - Chung, Sheng Chia
AU - Cicuttini, Flavia M.
AU - Cirillo, Massimo
AU - Cohen, Aaron J.
AU - Collado-Mateo, Daniel
AU - Cooper, Cyrus
AU - Cooper, Owen R.
AU - Coresh, Josef
AU - Cornaby, Leslie
AU - Cortesi, Paolo Angelo
AU - Cortinovis, Monica
AU - Costa, Megan
AU - Cousin, Ewerton
AU - Criqui, Michael H.
AU - Cromwell, Elizabeth A.
AU - Cundiff, David K.
AU - Daba, Alemneh Kabeta
AU - Dachew, Berihun Assefa
AU - Dadi, Abel Fekadu
AU - Damasceno, Albertino Antonio Moura
AU - Dandona, Lalit
AU - Dandona, Rakhi
AU - Darby, Sarah C.
AU - Dargan, Paul I.
AU - Daryani, Ahmad
AU - Das Gupta, Rajat
AU - Das Neves, José
AU - Dasa, Tamirat Tesfaye
AU - Dash, Aditya Prasad
AU - Davitoiu, Dragos Virgil
AU - Davletov, Kairat
AU - De la Cruz-Góngora, Vanessa
AU - De La Hoz, Fernando Pio
AU - De Leo, Diego
AU - De Neve, Jan Walter
AU - Degenhardt, Louisa
AU - Deiparine, Selina
AU - Dellavalle, Robert P.
AU - Deshpande, Aniruddha
AU - Demoz, Gebre Teklemariam
AU - Denova-Gutiérrez, Edgar
AU - Deribe, Kebede
AU - Dervenis, Nikolaos
AU - El Bcheraoui, Charbel
AU - Dharmaratne, Samath D.
AU - Estep, Kara
AU - Islam, Sheikh Mohammed Shariful
AU - Fay, Kairsten
AU - Feigin, Valery L
AU - Ferrara, Giannina
AU - Foreman, Kyle J.
AU - Fullman, Nancy
AU - Gardner, William
AU - Hawley, Caitlin
AU - Hay, Simon
AU - Hsiao, Thomas
AU - Huynh, Chantal
AU - Irvine, Caleb
AU - James, Spencer
AU - Kassebaum, N. J.
AU - Kemp, Grant R.
AU - Khalil, Ibrahim
AU - Krohn, Kristopher J.
AU - Kyu, Hmwe Hmwe
AU - Larson, Samantha L.
AU - Lopez, Alan D
AU - Lozano, Rafael
AU - Manguerra, Helena
AU - Marks, Ashley
AU - Millear, Anoushka I.
AU - Miller-Petrie, Molly K.
AU - Misganaw, Awoke T.
AU - Mokdad, Ali
AU - Muller, Kate
AU - Naghavi, Mohsen
AU - Nguyen, Grant
AU - Nguyen, Minh
AU - Nichols, Emma
AU - Nixon, Molly R.
AU - Nsoesie, Elaine
AU - Odell, Christopher M.
AU - Olsen, Helen E.
AU - Ong, Kanyin
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AU - Román , Yesenia
AU - Roth, Gregory A.
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AU - Vollset, Stein E
AU - Vos, Theo
AU - Whiteford, Harvey A.
AU - Yadgir, Simon
AU - Zimsen, Stephanie R. M.
AU - Murray, Christopher
AU - Shiferaw, Mekonnen S.
AU - Weldegebreal, Fitsum
AU - Mitiku, Habtamu
AU - Bali, Ayele Geleto
AU - Tekle, Merhawi
AU - Dasa, Tamirat Tesfaye
AU - Roba, Kedir
AU - Diro, Helen
AU - Gelano, Tilayie
AU - Hailegiyorgis, Tewodros
AU - Tekalign, Tigist
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AU - Duken, Eyasu Ejeta
AU - Hussen, Mamusha A.
AU - Mereta, Seid
AU - Irvani, Seyed S. N.
AU - Moghaddam, Sahar S.
AU - Shams-Beyranvand, Mehran
AU - Ebrahimi, Hedyeh
AU - Mohajer, Bahram
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AU - Pishgar, F.
AU - Esteghamati, Alireza
AU - Ganji, Morsaleh
AU - Mousavi, Seyyed M.
AU - Eskandarieh, Sharareh
AU - Sahraian, Mohammad
AU - Hafezi-Nejad, Nima
AU - Haj-Mirzaian, Arvin
AU - Hamadeh, Randah
AU - Hosseini, Seyed M.
AU - Mansournia, Mohammad A.
AU - Kinfu, Yohannes
N1 - Funding Information: Carl Abelardo Antonio reports personal fees from Johnson & Johnson (Philippines). Yannick Bejot reports grants and personal fees from AstraZeneca and Boehringer-Ingelheim and personal fees from Daiichi-Sankyo, Bristol-Myers Squibb (BMS), Pfizer, Medtronic, Bayer, Novex Pharma, and Merck Sharp & Dohme (MSD). Cyrus Cooper reports personal fees from Alliance for Better Bone Health, Amgen, Eli Lilly, GlaxoSmithKline (GSK), Medtronic, Merck & Co, Novartis, Pfizer, Roche, Servier, Takeda, and UCB. Seana Gall reports grants from the National Health and Medical Research Council and the National Heart Foundation of Australia. Panniyammakal Jeemon reports a Clinical and Public Health Intermediate Fellowship from the Wellcome Trust–DBT India Alliance (2015–20). Jacek Jóźwiak reports a grant from Valeant, personal fees from Valeant, ALAB Laboratoria and Amgen, and non-financial support from Microlife and Servier. Nicholas Kassebaum reports personal fees and other support from Vifor Pharmaceuticals. Srinivasa Vittal Katikireddi reports grants from NHS Research Scotland, the Medical Research Council, and the Scottish Government Chief Scientist Office. Jeffrey Lazarus reports personal fees from Janssen and Cepheid and grants and personal fees from AbbVie, Gilead Sciences, and MSD. Stefan Lorkowski reports personal fees from Amgen, Berlin-Chemie, MSD, Novo Nordisk, Sanofi-Aventis, Synlab, Unilever, and non-financial support from Preventicus. Winfried März reports grants and personal fees from Siemens Diagnostics, Aegerion Pharmaceuticals, Amgen, AstraZeneca, Danone Research, Pfizer, BASF, Numares, and Berline-Chemie; personal fees from Hoffmann LaRoche, MSD, Sanofi, and Synageva; grants from Abbott Diagnostics; and other support from Synlab. Walter Mendoza is currently a Program Analyst for Population and Development at the Peru Country Office of the United Nations Population Fund. Dariush Mozaffarian reports grants from National Institutes of Health (NIH) and the Bill & Melinda Gates Foundation; personal fees from Global Organization for EPA and DHA Omega-3s, DSM, Nutrition Impact, Pollock Communications, Bunge, Indigo Agriculture, Amarin, Acasti Pharma, and America's Test Kitchen; chapter royalties from UpToDate; they serve on the scientific advisory board for Omada Health, Elysium Health, and DayTwo. Maarten Postma reports grants from Mundipharma, Bayer, BMS, AstraZeneca, ARTEG, and AscA; grants and personal fees from Sigma Tau, MSD, GSK, Pfizer, Boehringer Ingelheim, Novavax, Ingress Health, AbbVie, and Sanofi; personal fees from Quintiles, Astellas, Mapi, OptumInsight, Novartis, Swedish Orphan, Innoval, Jansen, Intercept, and Pharmerit, and stock ownership in Ingress Health and Pharmacoeconomics Advice Groningen. Kenji Shibuya reports grants from the Ministry of Health, Labour, and Welfare and from the Ministry of Education, Culture, Sports, Science, and Technology. Mark Shrime reports grants from Mercy Ships and Damon Runyon Cancer Research Foundation. Jasvinder Singh reports consulting for Horizon, Fidia, UBM, Medscape, WebMD, the NIH, and the American College of Rheumatology; they serve as the principal investigator for an investigator-initiated study funded by Horizon pharmaceuticals through a grant to Dinora, a 501c3 entity; they are on the steering committee of OMERACT, an international organisation that develops measures for clinical trials and receives arms-length funding from 36 pharmaceutical companies. Cassandra Szoeke reports a grant from the National Medical Health Research Council, Lundbeck, Alzheimer's Association, and the Royal Australasian College of Practitioners; she holds patent PCT/AU2008/001556. Jeffrey Stanaway reports a grant from Merck & Co. Muthiah Vaduganathan receives research support from the NIH/National Heart, Lung, and Blood Institute and serves as a consultant for Bayer and Baxter Healthcare. Denis Xavier reports grants from Cadila Pharmaceuticals, Boehringer Ingelheim, Sanofi-Aventis, Pfizer, and BMS. All other authors declare no competing interests. Funding Information: Research reported in this publication was supported by the Bill & Melinda Gates Foundation, the University of Melbourne, Public Health England, the Norwegian Institute of Public Health, St Jude Children's Research Hospital, the National Institute on Ageing of the National Institutes of Health (NIH; award P30AG047845) , and the National Institute of Mental Health of NIH (award R01MH110163) . The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. We thank the Russia Longitudinal Monitoring Survey, done by National Research University Higher School of Economics and ZAO Demoscope together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology RAS, for making these data available. The Health Behaviour in School-Aged Children (HBSC) study is an international study carried out in collaboration with WHO/Europe. The International Coordinator of the 1997–98, 2001–02, 2005–06, and 2009–10 surveys was Candace Currie and the databank managers were Bente Wold for the 1997–98 survey and Oddrun Samdal for the following surveys. A list of principal investigators in each country can be found on the HBSC website . This research uses data from Add Health, a programme project designed by J Richard Udry, Peter S Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R Rindfuss and Barbara Entwisle for assistance in the original design of Add Health. People interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W Franklin Street, Chapel Hill, NC 27516–2524 ( [email protected] ). No direct support was received from grant P01-HD31921 for this analysis. Data for this research was provided by MEASURE Evaluation, funded by the United States Agency for International Development (USAID). Views expressed do not necessarily reflect those of USAID, the US Government, or MEASURE Evaluation. This research used data from the National Health Survey 2003. The authors are grateful to the Ministry of Health of Chile, the survey copyright owner, for allowing them to have the database. All results of the study are those of the authors and in no way committed to the Ministry. The Palestinian Central Bureau of Statistics granted the researchers of GBD 2017 access to relevant data in accordance with licence no SLN2014–3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law, 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. This paper uses data from Survey of Health, Ageing and Retirement in Europe ( SHARE Waves 1, 2, 3 (SHARELIFE), 4, 5, and 6 (DOIs: 10·6103/SHARE.w1.611, 10.6103/SHARE.w2.611, 10.6103/SHARE.w3.611, 10.6103/SHARE.w4.611, 10.6103/SHARE.w5.611, 10.6103/SHARE.w6.611), see Börsch-Supan and colleagues (2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001–00360), FP6 (SHARE-I3: RII-CT-2006–062193, COMPARE: CIT5-CT-2005–028857, SHARELIFE: CIT4-CT-2006–028812) and FP7 (SHARE-PREP: No 211909, SHARE-LEAP: No 227822, SHARE M4: No 261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_AG09740–13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553–01, IAG_BSR06–11, OGHA_04–064, and HHSN271201300071C) and from various national funding sources is gratefully acknowledged. This paper uses data from the WHO Study on global AGEing and adult health. Publisher Copyright: © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
PY - 2018/11/10
Y1 - 2018/11/10
N2 - Background: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings: In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning.
AB - Background: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings: In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning.
KW - Adolescent
KW - Adult
KW - Age Distribution
KW - Aged
KW - Aged, 80 and over
KW - Child
KW - Child, Preschool
KW - Disabled Persons/statistics & numerical data
KW - Environmental Exposure/adverse effects
KW - Female
KW - Global Burden of Disease/statistics & numerical data
KW - Global Health/statistics & numerical data
KW - Health Risk Behaviors
KW - Humans
KW - Infant
KW - Infant, Newborn
KW - Life Expectancy
KW - Male
KW - Metabolic Diseases/epidemiology
KW - Middle Aged
KW - Occupational Diseases/epidemiology
KW - Occupational Exposure/adverse effects
KW - Quality-Adjusted Life Years
KW - Risk Assessment
KW - Sex Distribution
KW - Socioeconomic Factors
KW - Young Adult
UR - http://www.scopus.com/inward/record.url?scp=85056201749&partnerID=8YFLogxK
U2 - 10.1016/S0140-6736(18)32225-6
DO - 10.1016/S0140-6736(18)32225-6
M3 - Article
C2 - 30496105
AN - SCOPUS:85056201749
SN - 0140-6736
VL - 392
SP - 1923
EP - 1994
JO - The Lancet
JF - The Lancet
IS - 10159
ER -